APPLYING THE MULTI-OBJECTIVE HYBRID BACTERIA FORAGING -PARTICLE SWARM OPTIMIZATION TO SPEED CONTROL OF PMSM MOTOR FOR ELECTRIC VEHICLES

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doi:10.34238/tnu-jst.10672

APPLYING THE MULTI-OBJECTIVE HYBRID BACTERIA FORAGING -PARTICLE SWARM OPTIMIZATION TO SPEED CONTROL OF PMSM MOTOR FOR ELECTRIC VEHICLES

About this article

Received: 28/06/24 Revised: 26/11/24 Published: 27/11/24

Authors

Le Dinh Hieu , School of Engineering and Technology - Hue University

Abstract

Electric vehicles are becoming a mainstream trend in the auto industry, due to their ability to reduce emissions to near zero and save energy. Not using a traditional internal combustion engine is an outstanding advantage of electric vehicles, operated by electric motors and batteries, bringing many environmental and economic benefits. Electric vehicles are mainly driven by permanent magnet synchronous motors with high efficiency, high power density, and the ability to operate smoothly and with little maintenance. This article proposes the BFPSO-PID multi-objective optimization algorithm to optimize the speed-stabilizing controller that controls the PMSM motor driving on electric vehicles. Electric vehicle speed control quality using the BFPSO-PID algorithm is evaluated through the vehicle's response to world standard driving cycles such as ECE-15, EUDC and NEDCE. The vehicle speed is evaluated and compared according to the proposed standard driving cycle between BFPSO-PID and PID speed control algorithms. The results show that the BFPSO-PID multi-objective speed optimization controller provides better control quality than the traditional PID controller.

Keywords

Electric vehicle; Bacterial foraging optimization; Particle swarm optimization; Driving cycle for electric vehicle; Multi-objective optimization

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DOI: https://doi.org/10.34238/tnu-jst.10672

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